Put Your Clinical Trials Data to Work with Applied Machine Learning

Clinical trials data is increasing in complexity as well as in volume day by day. Researchers are collecting a variety of data in form of text, numbers, images, voice recordings, video recordings, tapping speed, range of motion and many more in their search for new life saving treatments. Take for example the All of US Precision Medicine Initiative. This project aims to gather data on individual differences in lifestyle, environment, and biology from 1 million Americans to accelerate precision medicine work answer some basic questions about human life.

With limited time and limited capabilities of our eyes and brains, it is not humanly possible to analyse such huge data sets completely and draw useful conclusions. Moreover, we may miss out on important and beneficial conclusions. This is where machines step in. Industry experts estimate that machine learning in pharma and medicine could generate a value of up to $100B annually. Using advanced predictive analytics, machine learning systems have the potential to increase the efficiency of research, improve clinical trial management and make better decisions. Recently, scientists have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish.

What is Applied ML?

At DIA conference this year, we showcased our featured product Applied ML – a cutting edge Machine Learning solution for working with clinical trials data. Our CEO Dr Sharib Khan demoed one of the fun computer vision features that recognises people’s faces, predicts their age and gender then matches them to a celebrity. We showed how the same computer vision technology could be used to parse information from a protocol document using the Applied ML platform. The technology performs unsupervised detection of the boundaries and then the text is sent to a set of supervised text classifiers and named entity extraction algorithms that are able to identify the relevant details from the protocol document. The extracted entities could then be leveraged to solve a variety of protocol optimisation problems in terms of protocol design, feasibility planning and patient recruitment.

Dr Chintan Patel, computer scientist, informatician, and CTO at Applied Informatics presented this life science industry’s first end-to-end machine learning platform to accelerate drug development at the 1st AI Pharma Innovation: Clinical Development Summit. He was joined by high-profile industry experts at the forefront of digital transformation in life sciences, to share how AI and machine learning could revolutionize traditional drug development practices by leveraging the untapped potential of clinical trials data.

What can Applied ML do?

Applied ML is an innovative Machine learning platform to optimize clinical trial processes. It has the potential to predict various kinds of risks that investigators could come across in clinical trial management, and provide recommendations as and when needed. It helps answer several basic questions related to drug discovery, feasibility of a clinical trial and study start up. For example: What are potential new compounds for targets? How many sites would be needed for this study and which locations? How many participants would need to be enrolled for this study? …and so on.

It can find ways to collect, use and analyse data more efficiently and help investigators narrow down their target population so as to make trials quicker and inexpensive. With its mapping enabled to 150+ biochemical ontologies and vocabularies (HaVoc), 100+ out of box features tuned for clinical trials data, it can help dig through piles of data simultaneously to find out errors in informed consent documents, missing patient reports etc.

All-in-all, Applied ML is an end-to-end data science and machine learning platform to work with clinical trial data from life sciences systems. Dr Patel believes that Applied ML will help replace several, currently inefficient clinical trials processes with data-driven, self-optimizing AI processes.